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In [ ]:
MEASURE_FOLDER = os.path.join(EXP_FOLDER, '28SeptFullRun')
df = get_data_frame(MEASURE_FOLDER)
df.drop(columns='betaPAErr', inplace=True)
df.dropna(inplace=True)

dfc = df.copy()

100%|██████████| 601/601 [22:10<00:00,  2.21s/it]
In [ ]:
df_grouped = df.groupby(by=['pump_reference', 'pump_AOM_freq'])
groups = dict(list(df_grouped))
dfs = [df for df in groups.values()]

max_freqs = [384183.3]*len(dfs)
zipped_data = list(zip(dfs, max_freqs))

fig1, ax1s = plt.subplots(5)
fig2, ax2s = plt.subplots(3)

for i, (df, max_freq)  in enumerate(zipped_data[:]):
    j1 = i//3
    j2 = i%3
    
    data = df.dropna()
    freqs = ((max_freq-PUMP_FREQUENCY)-(data['tempV']-data['tempV'].min())*FREQVSVOLT- (data['currV']-0.0)*FREQVSCURR)
    ax2s[j2] = plot_spline_fit(ax2s[j2], x=freqs, y=data['ratio'], yerr=data['ratioErr'],scolor=f'C{j1}', mfc=f'C{j1}',color=f'C{j1}', s=0.0, ms=5, figsize=(5, 25), linewidth=1.5, label=f"Detuning  = { 180-2*df.iloc[0]['pump_AOM_freq'] :.2f}", fig=fig2)
    ax2s[j2].set_title(f"Pump Amplituide = { df.iloc[10]['pump_reference'] :.2f}", **titledict)
    ax2s[j2].legend()
    
    ax1s[j1] = plot_spline_fit(ax1s[j1], x=freqs, y=data['ratio'], yerr=data['ratioErr'],scolor=f'C{j2}', mfc=f'C{j2}',color=f'C{j2}', s=0.0, ms=5, figsize=(5, 25), linewidth=1.5, label=f"Pump Amplitude = { df.iloc[0]['pump_reference'] :.2f}", fig=fig1)
    ax1s[j1].set_title(f"Detuning  = { 180-2*df.iloc[10]['pump_AOM_freq'] :.2f}", **titledict)
    ax1s[j1].legend()
    
fig1.tight_layout()
fig2.tight_layout()

fig1.savefig(os.path.join(MEASURE_FOLDER, 'lossFeaturesDet.png'))
plt.show()
plt.close()

fig2.savefig(os.path.join(MEASURE_FOLDER, 'lossFeaturesPA.png'))
plt.show()
plt.close()
In [ ]:
df = dfc.copy()
df_grouped = df.groupby(by=['pump_reference', 'pump_AOM_freq'])
groups = dict(list(df_grouped))
dfs = [df for df in groups.values()]

max_freqs = [384183.3]*len(dfs)
zipped_data = list(zip(dfs, max_freqs))

fig1, ax1s = plt.subplots(5)
fig2, ax2s = plt.subplots(3)

for i, (df, max_freq)  in enumerate(zipped_data[:]):
    j1 = i%5
    j2 = i//5
    
    data = df.dropna()
    freqs = ((max_freq-PUMP_FREQUENCY)-(data['tempV']-data['tempV'].min())*FREQVSVOLT- (data['currV']-0.0)*FREQVSCURR)
    ax2s[j2] = plot_spline_fit(ax2s[j2], x=freqs, y=data['ratio'], yerr=data['ratioErr'],scolor=f'C{j1}', mfc=f'C{j1}',color=f'C{j1}', s=0.0, ms=5, figsize=(5, 25), linewidth=1.5, label=f"Detuning  = { 180-2*df.iloc[0]['pump_AOM_freq'] :.2f}", fig=fig2)
    ax2s[j2].set_title(f"Pump Amplituide = { df.iloc[0]['pump_reference'] :.2f}", **titledict)
    ax2s[j2].legend()
    
    ax1s[j1] = plot_spline_fit(ax1s[j1], x=freqs, y=data['ratio'], yerr=data['ratioErr'],scolor=f'C{j2}', mfc=f'C{j2}',color=f'C{j2}', s=0.0, ms=5, figsize=(5, 25), linewidth=1.5, label=f"Pump Amplitude = { df.iloc[0]['pump_reference'] :.2f}", fig=fig1)
    ax1s[j1].set_title(f"Detuning  = { 180-2*df.iloc[0]['pump_AOM_freq'] :.2f}", **titledict)
    ax1s[j1].legend()
    
fig1.tight_layout()
fig2.tight_layout()

fig1.savefig(os.path.join(MEASURE_FOLDER, 'lossFeaturesDet.png'))
plt.show()
plt.close()

fig2.savefig(os.path.join(MEASURE_FOLDER, 'lossFeaturesPampl.png'))
plt.show()
plt.close()
In [ ]:
df = dfc.copy()
df_grouped = df.groupby(by=['pump_reference', 'pump_AOM_freq'])
groups = dict(list(df_grouped))
dfs = [df for df in groups.values()]

max_freqs = [384183.3]*len(dfs)
zipped_data = list(zip(dfs, max_freqs))

fig1, ax1s = plt.subplots(5)
fig2, ax2s = plt.subplots(3)
fig1.set_size_inches(6, 20)
fig2.set_size_inches(6, 20)

for i, (df, max_freq)  in enumerate(zipped_data[:]):
    j1 = i%5
    j2 = i//5
    
    data = df.dropna()
    freqs = ((max_freq-PUMP_FREQUENCY)-(data['tempV']-data['tempV'].min())*FREQVSVOLT- (data['currV']-0.0)*FREQVSCURR)
    
    betaPAs = [a for a,b in sorted(zip(data['betaPA'], freqs), key=lambda pair:pair[1])]
    freqs = sorted(freqs)
 
    ax2s[j2].plot(freqs, betaPAs, 'o-', color=f'C{j1}', ms=5, label=f"Detuning  = { 180-2*df.iloc[0]['pump_AOM_freq'] :.2f}")
    ax2s[j2].set_title(f"Pump Amplituide = { df.iloc[0]['pump_reference'] :.2f}", **titledict)
    ax2s[j2].legend()
    
    ax1s[j1].plot(freqs, betaPAs, 'o-', color=f'C{j2}', ms=5, label=f"Pump Amplitude = { df.iloc[0]['pump_reference'] :.2f}")
    ax1s[j1].set_title(f"Detuning  = { 180-2*df.iloc[0]['pump_AOM_freq'] :.2f}", **titledict)
    ax1s[j1].legend()
    
fig1.tight_layout()
fig2.tight_layout()

#fig1.savefig(os.path.join(MEASURE_FOLDER, 'lossFeaturesDet.png'))
plt.show()
plt.close()

#fig2.savefig(os.path.join(MEASURE_FOLDER, 'lossFeaturesPampl.png'))
plt.show()
plt.close()
In [ ]:
dfc[(dfc['pump_reference'] == 0.7625)]
Out[ ]:
sampleRate extraTime timeHold timeBaseline timeTest timeLoad timeF1 offset baseVolt BaseVoltErr ... precut_t filtertime master_clear tempV currV cat_AOM_freq cat_AOM_ampl cat_deload_t MOT_reload_t timestamp

0 rows × 118 columns

In [ ]:
dfc[(dfc['pump_AOM_freq'] == 84.0) & (dfc['pump_reference'] < 0.8)][['betaPA', 'timestamp'] ]
Out[ ]:
betaPA timestamp
51 3.309983 1900-01-01 01:01:54
52 0.450151 1900-01-01 01:03:06
53 0.287392 1900-01-01 01:04:18
54 0.119637 1900-01-01 01:05:30
55 0.208384 1900-01-01 01:06:42
56 0.368674 1900-01-01 01:07:54
57 0.959289 1900-01-01 01:09:06
58 0.041497 1900-01-01 01:10:18
59 3.406206 1900-01-01 01:11:30
60 0.318688 1900-01-01 01:12:42
61 0.055014 1900-01-01 01:13:54
62 0.123487 1900-01-01 01:15:06
63 0.796368 1900-01-01 01:16:18
64 0.411875 1900-01-01 01:17:30
65 0.802015 1900-01-01 01:18:42
66 0.640599 1900-01-01 01:19:54
67 0.417335 1900-01-01 01:21:06
68 0.566864 1900-01-01 01:22:18
69 0.524326 1900-01-01 01:23:30
70 0.253015 1900-01-01 01:24:42
71 0.082638 1900-01-01 01:25:54
72 0.191348 1900-01-01 01:27:06
73 0.187672 1900-01-01 01:28:18
74 0.103866 1900-01-01 01:29:30
75 0.365836 1900-01-01 01:30:42
76 4.139996 1900-01-01 01:31:54
77 0.271457 1900-01-01 01:33:06
78 0.137750 1900-01-01 01:34:18
79 2.915203 1900-01-01 01:35:30
80 3.903975 1900-01-01 01:36:42
81 0.278113 1900-01-01 01:37:54
82 1.177718 1900-01-01 01:39:06
83 0.359210 1900-01-01 01:40:18
84 2.186490 1900-01-01 01:41:30
85 0.199127 1900-01-01 01:42:42
86 0.382669 1900-01-01 01:43:54
87 0.699960 1900-01-01 01:45:06
88 0.300958 1900-01-01 01:46:18
89 0.474162 1900-01-01 01:47:31
90 0.195849 1900-01-01 01:48:43
In [ ]:
dfc[(dfc['pump_AOM_freq'] == 83.0) & (dfc['pump_reference'] < 0.8)][['betaPA', 'timestamp'] ]
Out[ ]:
betaPA timestamp
531 1.774020e-01 1900-01-01 22:27:15
532 1.760921e+00 1900-01-01 22:28:35
533 3.240879e-01 1900-01-01 22:30:16
534 1.182990e+00 1900-01-01 22:31:36
535 3.531493e-01 1900-01-01 22:33:01
536 5.326628e-12 1900-01-01 22:34:30
537 7.749401e-01 1900-01-01 22:35:57
538 1.531048e-07 1900-01-01 22:37:24
539 5.018208e-13 1900-01-01 22:38:51
540 1.542404e+01 1900-01-01 22:40:17
541 6.927596e-01 1900-01-01 22:41:44
542 2.187805e-12 1900-01-01 22:43:12
543 4.462498e-01 1900-01-01 22:44:37
544 2.322788e-09 1900-01-01 22:46:03
545 3.123936e+00 1900-01-01 22:47:33
546 3.040901e-12 1900-01-01 22:49:01
547 3.574465e-10 1900-01-01 22:50:27
548 4.965897e-01 1900-01-01 22:51:54
549 6.892042e-12 1900-01-01 22:53:21
550 1.854949e+00 1900-01-01 22:54:47
551 6.866640e-01 1900-01-01 22:56:14
552 7.838930e+00 1900-01-01 22:57:42
553 3.430268e+00 1900-01-01 22:59:09
554 3.950584e+00 1900-01-01 23:00:38
555 2.108447e+00 1900-01-01 23:02:03
556 7.435618e-08 1900-01-01 23:03:28
557 2.089072e-01 1900-01-01 23:04:55
558 3.628473e+00 1900-01-01 23:06:22
559 4.131470e-08 1900-01-01 23:07:49
560 4.336303e-09 1900-01-01 23:09:16
561 1.072106e-09 1900-01-01 23:10:42
562 6.413689e-01 1900-01-01 23:12:09
563 1.993138e-01 1900-01-01 23:13:39
564 2.053789e+00 1900-01-01 23:15:06
565 4.842033e-08 1900-01-01 23:16:33
566 5.991808e-01 1900-01-01 23:18:01
567 2.106691e-02 1900-01-01 23:19:28
568 2.684074e-01 1900-01-01 23:20:55
569 9.570122e-14 1900-01-01 23:22:23
570 1.400616e-01 1900-01-01 23:23:49
In [ ]:
dh1 = load_single_run('C:\Users\svars\OneDrive\Desktop\UBC Lab\CATExperiment\CATMeasurements\28SeptFullRun\22-40-17')

  Cell In[29], line 1
    dh1 = load_single_run('C:\Users\svars\OneDrive\Desktop\UBC Lab\CATExperiment\CATMeasurements\28SeptFullRun\22-40-17')
                                                                                                                        ^
SyntaxError: (unicode error) 'unicodeescape' codec can't decode bytes in position 2-3: truncated \UXXXXXXXX escape
In [ ]:
dh1 = load_single_run(r'C:\Users\svars\OneDrive\Desktop\UBC Lab\CATExperiment\CATMeasurements\28SeptFullRun\22-40-17')
In [ ]:
dfc[(dfc['pump_AOM_freq'] == 83.0) & (dfc['pump_reference'] < 1.14) & (dfc['pump_reference'] > 0.9)][['betaPA', 'timestamp'] ]
Out[ ]:
betaPA timestamp
0 6.199216e-02 1900-01-01 00:00:39
1 2.334133e-12 1900-01-01 00:01:51
2 1.657226e-09 1900-01-01 00:03:03
3 1.084179e-01 1900-01-01 00:04:15
4 1.507625e-03 1900-01-01 00:05:27
5 7.411405e-12 1900-01-01 00:06:38
6 3.522036e-01 1900-01-01 00:07:51
7 1.633247e-08 1900-01-01 00:09:03
8 5.639933e-14 1900-01-01 00:10:16
9 1.097438e-02 1900-01-01 00:11:28
10 5.589925e-02 1900-01-01 00:12:40
571 4.405692e-02 1900-01-01 23:25:15
572 2.502242e-01 1900-01-01 23:26:43
573 1.327300e+00 1900-01-01 23:27:59
574 1.840282e+00 1900-01-01 23:29:14
575 9.088021e-09 1900-01-01 23:30:28
576 3.724222e-01 1900-01-01 23:31:41
577 1.555463e+00 1900-01-01 23:32:55
578 6.801906e-03 1900-01-01 23:34:08
579 1.371795e-01 1900-01-01 23:35:21
580 4.032927e-01 1900-01-01 23:36:34
581 1.841765e-10 1900-01-01 23:37:47
582 5.884259e-02 1900-01-01 23:39:00
583 2.520008e+00 1900-01-01 23:40:12
584 1.097229e-08 1900-01-01 23:41:25
585 7.841562e-01 1900-01-01 23:42:37
586 3.723999e-07 1900-01-01 23:43:50
587 5.608497e-01 1900-01-01 23:45:02
588 3.804618e-01 1900-01-01 23:46:14
589 2.863642e-01 1900-01-01 23:47:27
590 1.217655e-01 1900-01-01 23:48:39
591 1.796249e-03 1900-01-01 23:49:51
592 6.044906e-01 1900-01-01 23:51:03
593 2.246985e-01 1900-01-01 23:52:15
594 7.801304e-07 1900-01-01 23:53:27
595 8.649304e-01 1900-01-01 23:54:39
596 1.536864e-08 1900-01-01 23:55:51
597 1.592029e-09 1900-01-01 23:57:03
598 9.058798e-11 1900-01-01 23:58:15
599 3.779783e-09 1900-01-01 23:59:27
In [ ]:
dfc[(dfc['pump_AOM_freq'] == 83.0) & (dfc['pump_reference'] < 1.14) & (dfc['pump_reference'] > 0.9)][['betaPA', 'timestamp'] ][dfc['betaPA']>1]

<ipython-input-32-a2d324bca11b>:1: UserWarning: Boolean Series key will be reindexed to match DataFrame index.
  dfc[(dfc['pump_AOM_freq'] == 83.0) & (dfc['pump_reference'] < 1.14) & (dfc['pump_reference'] > 0.9)][['betaPA', 'timestamp'] ][dfc['betaPA']>1]
Out[ ]:
betaPA timestamp
573 1.327300 1900-01-01 23:27:59
574 1.840282 1900-01-01 23:29:14
577 1.555463 1900-01-01 23:32:55
583 2.520008 1900-01-01 23:40:12
In [ ]:
dfc[(dfc['pump_AOM_freq'] == 83.0) & (dfc['pump_reference'] < 1.14) & (dfc['pump_reference'] > 0.9)][['betaPA', 'ratio', 'timestamp'] ][dfc['betaPA']>1]

<ipython-input-33-a78d912e18c8>:1: UserWarning: Boolean Series key will be reindexed to match DataFrame index.
  dfc[(dfc['pump_AOM_freq'] == 83.0) & (dfc['pump_reference'] < 1.14) & (dfc['pump_reference'] > 0.9)][['betaPA', 'ratio', 'timestamp'] ][dfc['betaPA']>1]
Out[ ]:
betaPA ratio timestamp
573 1.327300 0.628378 1900-01-01 23:27:59
574 1.840282 0.564625 1900-01-01 23:29:14
577 1.555463 0.565066 1900-01-01 23:32:55
583 2.520008 0.474172 1900-01-01 23:40:12
In [ ]:
dfc[(dfc['pump_AOM_freq'] == 85.0) & (dfc['pump_reference'] < 1.14) & (dfc['pump_reference'] > 0.9)][['betaPA', 'ratio', 'timestamp'] ][dfc['betaPA']>1]

<ipython-input-34-365b98efa95f>:1: UserWarning: Boolean Series key will be reindexed to match DataFrame index.
  dfc[(dfc['pump_AOM_freq'] == 85.0) & (dfc['pump_reference'] < 1.14) & (dfc['pump_reference'] > 0.9)][['betaPA', 'ratio', 'timestamp'] ][dfc['betaPA']>1]
Out[ ]:
betaPA ratio timestamp
212 1.015023 0.512176 1900-01-01 04:15:03
241 1.553289 0.422792 1900-01-01 04:49:53
In [ ]:
dfc[(dfc['pump_AOM_freq'] == 84.0) & (dfc['pump_reference'] < 1.14) & (dfc['pump_reference'] > 0.9)][['betaPA', 'ratio', 'timestamp'] ][dfc['betaPA']>0.8]

<ipython-input-35-75505a28962d>:1: UserWarning: Boolean Series key will be reindexed to match DataFrame index.
  dfc[(dfc['pump_AOM_freq'] == 84.0) & (dfc['pump_reference'] < 1.14) & (dfc['pump_reference'] > 0.9)][['betaPA', 'ratio', 'timestamp'] ][dfc['betaPA']>0.8]
Out[ ]:
betaPA ratio timestamp
98 1.213540 0.469758 1900-01-01 01:58:19
105 1.437785 0.450850 1900-01-01 02:06:43
110 0.952900 0.542567 1900-01-01 02:12:44
127 1.561206 0.449689 1900-01-01 02:33:06
In [ ]:
dfc[(dfc['pump_AOM_freq'] == 84.0) & (dfc['pump_reference'] > 1.14) & (dfc['pump_reference'] > 1.3)][['betaPA', 'ratio', 'timestamp'] ][dfc['betaPA']>0.8]

<ipython-input-36-1407e5c5e2cb>:1: UserWarning: Boolean Series key will be reindexed to match DataFrame index.
  dfc[(dfc['pump_AOM_freq'] == 84.0) & (dfc['pump_reference'] > 1.14) & (dfc['pump_reference'] > 1.3)][['betaPA', 'ratio', 'timestamp'] ][dfc['betaPA']>0.8]
Out[ ]:
betaPA ratio timestamp
In [ ]:
dfc[(dfc['pump_AOM_freq'] == 84.0) & (dfc['pump_reference'] > 1.14) & (dfc['pump_reference'] > 1.3)][['betaPA', 'ratio', 'timestamp'] ][dfc['betaPA']>0.3]

<ipython-input-37-14166250351b>:1: UserWarning: Boolean Series key will be reindexed to match DataFrame index.
  dfc[(dfc['pump_AOM_freq'] == 84.0) & (dfc['pump_reference'] > 1.14) & (dfc['pump_reference'] > 1.3)][['betaPA', 'ratio', 'timestamp'] ][dfc['betaPA']>0.3]
Out[ ]:
betaPA ratio timestamp
132 0.396203 0.657676 1900-01-01 02:39:06
153 0.734277 0.528556 1900-01-01 03:04:19
158 0.538092 0.605325 1900-01-01 03:10:20
163 0.511799 0.639684 1900-01-01 03:16:20
In [ ]:
dfc[(dfc['pump_AOM_freq'] == 83.0) & (dfc['pump_reference'] > 1.14) & (dfc['pump_reference'] > 1.3)][['betaPA', 'ratio', 'timestamp'] ][dfc['betaPA']>0.3]

<ipython-input-38-1dde2157d593>:1: UserWarning: Boolean Series key will be reindexed to match DataFrame index.
  dfc[(dfc['pump_AOM_freq'] == 83.0) & (dfc['pump_reference'] > 1.14) & (dfc['pump_reference'] > 1.3)][['betaPA', 'ratio', 'timestamp'] ][dfc['betaPA']>0.3]
Out[ ]:
betaPA ratio timestamp
13 0.366904 0.721244 1900-01-01 00:16:16
20 0.514714 0.668415 1900-01-01 00:24:42
37 0.923729 0.576031 1900-01-01 00:45:06
40 0.553775 0.636621 1900-01-01 00:48:42
In [ ]:
dfc[(dfc['pump_AOM_freq'] == 86.5) & (dfc['pump_reference'] <0.8) ][['betaPA', 'ratio', 'timestamp'] ][dfc['betaPA']>0.3]

<ipython-input-40-9166273068c8>:1: UserWarning: Boolean Series key will be reindexed to match DataFrame index.
  dfc[(dfc['pump_AOM_freq'] == 86.5) & (dfc['pump_reference'] <0.8) ][['betaPA', 'ratio', 'timestamp'] ][dfc['betaPA']>0.3]
Out[ ]:
betaPA ratio timestamp
416 0.441389 0.781138 1900-01-01 08:20:13
418 0.720811 0.697746 1900-01-01 08:22:37
432 0.487096 0.762030 1900-01-01 08:39:26
440 2.001106 0.496847 1900-01-01 08:49:02
447 1.545051 0.524370 1900-01-01 08:57:26
In [ ]:
# Plots for pampl 1.12, 1.49 and for pretty much all detunings (checked explictely for 7.0) make sense